{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check `GDS` stack\n",
    "\n",
    "This notebook checks all software requirements for the Open-Source Spatial Statistics workshop at AAG 2018. \n",
    "\n",
    "A successful run of the notebook implies no errors returned in any cell *and* every cell beyond the first one returning a printout of `True`. This ensures a correct environment installed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import bokeh as bk\n",
    "float(bk.__version__[:4]) >= 0.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dask\n",
    "float(dask.__version__[:4]) >= 0.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "float(gpd.__version__[:3]) >= 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "float(mpl.__version__[:3]) >= 1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import networkx as nx\n",
    "float(mpl.__version__[:3]) >= 2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "float(sns.__version__[:3]) >= 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "float(pd.__version__[:4]) >= 0.18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hu/anaconda3/envs/gds/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pymc3 as pm\n",
    "float(pm.__version__[:3]) >= 3.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pysal as ps\n",
    "float(ps.version[:4]) >= 1.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pystan\n",
    "float(pystan.__version__[:4]) >= 2.17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import rasterio as rio\n",
    "float(ps.version[:4]) >= 0.36"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sklearn\n",
    "float(sklearn.__version__[:4]) >= 0.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/hu/anaconda3/envs/gds/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "float(sm.version.version[:3]) >= 0.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlrd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xlsxwriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AREA</th>\n",
       "      <th>PERIMETER</th>\n",
       "      <th>COLUMBUS_</th>\n",
       "      <th>COLUMBUS_I</th>\n",
       "      <th>POLYID</th>\n",
       "      <th>NEIG</th>\n",
       "      <th>HOVAL</th>\n",
       "      <th>INC</th>\n",
       "      <th>CRIME</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>...</th>\n",
       "      <th>DISCBD</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>NSA</th>\n",
       "      <th>NSB</th>\n",
       "      <th>EW</th>\n",
       "      <th>CP</th>\n",
       "      <th>THOUS</th>\n",
       "      <th>NEIGNO</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.309441</td>\n",
       "      <td>2.440629</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>80.467003</td>\n",
       "      <td>19.531</td>\n",
       "      <td>15.725980</td>\n",
       "      <td>2.850747</td>\n",
       "      <td>...</td>\n",
       "      <td>5.03</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>44.070000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>POLYGON ((8.624129295349121 14.23698043823242,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.259329</td>\n",
       "      <td>2.236939</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>44.567001</td>\n",
       "      <td>21.232</td>\n",
       "      <td>18.801754</td>\n",
       "      <td>5.296720</td>\n",
       "      <td>...</td>\n",
       "      <td>4.27</td>\n",
       "      <td>35.619999</td>\n",
       "      <td>42.380001</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>POLYGON ((8.252790451049805 14.23694038391113,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.192468</td>\n",
       "      <td>2.187547</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>26.350000</td>\n",
       "      <td>15.956</td>\n",
       "      <td>30.626781</td>\n",
       "      <td>4.534649</td>\n",
       "      <td>...</td>\n",
       "      <td>3.89</td>\n",
       "      <td>39.820000</td>\n",
       "      <td>41.180000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>POLYGON ((8.653305053710938 14.00809001922607,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.083841</td>\n",
       "      <td>1.427635</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>33.200001</td>\n",
       "      <td>4.477</td>\n",
       "      <td>32.387760</td>\n",
       "      <td>0.394427</td>\n",
       "      <td>...</td>\n",
       "      <td>3.70</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>40.520000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>POLYGON ((8.459499359130859 13.82034969329834,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.488888</td>\n",
       "      <td>2.997133</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>23.225000</td>\n",
       "      <td>11.252</td>\n",
       "      <td>50.731510</td>\n",
       "      <td>0.405664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.83</td>\n",
       "      <td>40.009998</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>POLYGON ((8.685274124145508 13.63951969146729,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       AREA  PERIMETER  COLUMBUS_  COLUMBUS_I  POLYID  NEIG      HOVAL  \\\n",
       "0  0.309441   2.440629          2           5       1     5  80.467003   \n",
       "1  0.259329   2.236939          3           1       2     1  44.567001   \n",
       "2  0.192468   2.187547          4           6       3     6  26.350000   \n",
       "3  0.083841   1.427635          5           2       4     2  33.200001   \n",
       "4  0.488888   2.997133          6           7       5     7  23.225000   \n",
       "\n",
       "      INC      CRIME      OPEN  \\\n",
       "0  19.531  15.725980  2.850747   \n",
       "1  21.232  18.801754  5.296720   \n",
       "2  15.956  30.626781  4.534649   \n",
       "3   4.477  32.387760  0.394427   \n",
       "4  11.252  50.731510  0.405664   \n",
       "\n",
       "                         ...                          DISCBD          X  \\\n",
       "0                        ...                            5.03  38.799999   \n",
       "1                        ...                            4.27  35.619999   \n",
       "2                        ...                            3.89  39.820000   \n",
       "3                        ...                            3.70  36.500000   \n",
       "4                        ...                            2.83  40.009998   \n",
       "\n",
       "           Y  NSA  NSB   EW   CP   THOUS  NEIGNO  \\\n",
       "0  44.070000  1.0  1.0  1.0  0.0  1000.0  1005.0   \n",
       "1  42.380001  1.0  1.0  0.0  0.0  1000.0  1001.0   \n",
       "2  41.180000  1.0  1.0  1.0  0.0  1000.0  1006.0   \n",
       "3  40.520000  1.0  1.0  0.0  0.0  1000.0  1002.0   \n",
       "4  38.000000  1.0  1.0  1.0  0.0  1000.0  1007.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((8.624129295349121 14.23698043823242,...  \n",
       "1  POLYGON ((8.252790451049805 14.23694038391113,...  \n",
       "2  POLYGON ((8.653305053710938 14.00809001922607,...  \n",
       "3  POLYGON ((8.459499359130859 13.82034969329834,...  \n",
       "4  POLYGON ((8.685274124145508 13.63951969146729,...  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "shp = ps.examples.get_path('columbus.shp')\n",
    "db = gpd.read_file(shp)\n",
    "db.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "db[['HOVAL', 'INC']].to_feather('db_test.feather')\n",
    "_ = pd.read_feather('db_test.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "f, ax = plt.subplots(1)\n",
    "db.plot(facecolor='red', edgecolor='yellow', ax=ax)\n",
    "ax.set_axis_off()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "db.crs['init'] = 'epsg:26918'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db_wgs84 = db.to_crs(epsg=4326)\n",
    "db_wgs84.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.plot(column='INC', scheme='fisher_jenks', cmap=plt.matplotlib.cm.Blues)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import contextily as ctx\n",
    "tl = ctx.tile_providers.ST_TONER_LITE\n",
    "\n",
    "db = gpd.read_file(ps.examples.get_path('us48.shp'))\n",
    "db.crs = {'init': 'epsg:4326'}\n",
    "w, s, e, n = db.to_crs(epsg=3857).unary_union.bounds\n",
    "dbp = db.to_crs(epsg=3857)\n",
    "# Download raster\n",
    "_ = ctx.bounds2raster(w, s, e, n, 4, 'us.tif', url=tl)\n",
    "# Load up and plot\n",
    "source = rio.open('us.tif', 'r')\n",
    "red = source.read(1)\n",
    "green = source.read(2)\n",
    "blue = source.read(3)\n",
    "pix = np.dstack((red, green, blue))\n",
    "bounds = (source.bounds.left, source.bounds.right, \\\n",
    "          source.bounds.bottom, source.bounds.top)\n",
    "f = plt.figure(figsize=(6, 6))\n",
    "ax = plt.imshow(pix, extent=bounds)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
